PROJECT SUMMARY Globally, tuberculosis (TB) kills more people each year than any other infectious disease, but around 40% of TB cases go undiagnosed each year. This leads to continued transmission and a slow rate of decline in global TB incidence. Community-based TB screening interventions are one strategy for increasing the early diagnosis and treatment of people with tuberculosis. Knowledge is lacking on when community-based TB screening interventions are likely to be fruitful and on the impact of these interventions on TB transmission. This study addresses the first of these knowledge gaps by identifying epidemiologic signals that predict high yields for community-based TB screening, and by assessing barriers to optimal implementation of community-based screening programs. It addresses the second knowledge gap by measuring transmission within communities that receive TB screening interventions, and by identifying potential sites of transmission both within and outside the community. Aim 1 applies random forest regression to data from a community-based screening program to create a decision tree that uses information about past TB patients to predict whether a commnity is likely to have high levels of undiagnosed TB in the present. Aim 2 is a mixed-methods study focusing on people who were missed by TB screening interventions, which will help understand the barriers to successful implementation. Aim 3 uses whole-genome sequencing of clinical isolates to determine the proportion of TB cases attributable to recent transmission within intervention communities, using this metric to evaluate the impact of screening interventions on transmission. Aim 4 uses social network analysis to identify sites of transmission within and outside intervention communities. The long-term goal of this work is to reduce global TB morbidity through improved community-based screening.